Developing an IPF Prognostic Model and Screening for Key Genes Based on Cold Exposure-Related Genes Using Bioinformatics Approaches.

IF 3.9 3区 工程技术 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Peiyao Luo, Quankuan Gu, Jianpeng Wang, Xianglin Meng, Mingyan Zhao
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Abstract

Background: Cold exposure has an impact on various respiratory diseases. However, its relationship with idiopathic pulmonary fibrosis (IPF) remains to be elucidated. In this study, bioinformatics methods were utilized to explore the potential link between cold exposure and IPF. Methods: Cold exposure-related genes (CERGs) were identified using RNA-Seq data from mice exposed to cold versus room temperature conditions, along with cross-species orthologous gene conversion. Consensus clustering analysis was performed based on the CERGs. A prognostic model was established using univariate and multivariate risk analyses, as well as Lasso-Cox analysis. Differential analysis, WGCNA, and Lasso-Cox methods were employed to screen for signature genes. Results: This study identified 151 CERGs. Clustering analysis based on these CERGs revealed that IPF patients could be divided into two subgroups with differing severity levels. Significant differences were observed between these two subgroups in terms of hypoxia score, EMT score, GAP score, immune infiltration patterns, and mortality rates. A nine-gene prognostic model for IPF was established based on the CERG (AUC: 1 year: 0.81, 3 years: 0.79, 5 years: 0.91), which outperformed the GAP score (AUC: 1 year: 0.66, 3 years: 0.75, 5 years: 0.72) in prognostic accuracy. IPF patients were classified into high-risk and low-risk groups based on the RiskScore from the prognostic model, with significant differences observed between these groups in hypoxia score, EMT score, GAP score, immune infiltration patterns, and mortality rates. Ultimately, six high-risk signature genes associated with cold exposure in IPF were identified: GASK1B, HRK1, HTRA1, KCNN4, MMP9, and SPP1. Conclusions: This study suggests that cold exposure may be a potential environmental factor contributing to the progression of IPF. The prognostic model built upon cold exposure-related genes provides an effective tool for assessing the severity of IPF patients. Meanwhile, GASK1B, HRK1, HTRA1, KCNN4, MMP9, and SPP1 hold promise as potential biomarkers and therapeutic targets for IPF.

利用生物信息学方法建立IPF预后模型并筛选基于冷暴露相关基因的关键基因。
背景:冷暴露对各种呼吸系统疾病有影响。然而,其与特发性肺纤维化(IPF)的关系仍有待阐明。本研究利用生物信息学方法探讨了低温暴露与IPF之间的潜在联系。方法:利用暴露于低温和室温条件下小鼠的RNA-Seq数据,以及跨物种同源基因转换,鉴定冷暴露相关基因(cerg)。基于cerg进行一致性聚类分析。采用单因素、多因素风险分析及Lasso-Cox分析建立预后模型。采用差异分析、WGCNA和Lasso-Cox方法筛选特征基因。结果:本研究共鉴定出151个cerg。基于这些cerg的聚类分析显示,IPF患者可分为两个严重程度不同的亚组。两个亚组在缺氧评分、EMT评分、GAP评分、免疫浸润模式和死亡率方面存在显著差异。基于CERG建立IPF九基因预后模型(AUC: 1年:0.81,3年:0.79,5年:0.91),其预后准确性优于GAP评分(AUC: 1年:0.66,3年:0.75,5年:0.72)。根据预后模型的RiskScore将IPF患者分为高危组和低危组,各组之间在缺氧评分、EMT评分、GAP评分、免疫浸润模式和死亡率方面存在显著差异。最终,我们确定了IPF中与冷暴露相关的6个高危特征基因:GASK1B、HRK1、HTRA1、KCNN4、MMP9和SPP1。结论:本研究提示低温暴露可能是促进IPF进展的潜在环境因素。基于冷暴露相关基因的预后模型为评估IPF患者的严重程度提供了有效的工具。同时,GASK1B、HRK1、HTRA1、KCNN4、MMP9和SPP1有望成为IPF的潜在生物标志物和治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomedicines
Biomedicines Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.20
自引率
8.50%
发文量
2823
审稿时长
8 weeks
期刊介绍: Biomedicines (ISSN 2227-9059; CODEN: BIOMID) is an international, scientific, open access journal on biomedicines published quarterly online by MDPI.
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